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Fundamentals and Advances in Biometrics and Face Recognition

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Machine-based Intelligent Face Recognition

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Abstract

In this chapter, we mainly focus on the fundamentals and advances in the research of biometric recognition. In section 1, generalized biometric procedures and categories are defined and overviewed. This is followed in section 2 by the brief introduction and surveys on current cognitive science research. The essential point here is the fundamental intelligence of human brains. In section 3, machine-based biometric recognition tasks and methods are explored. As the marketing leader, fingerprint recognition is exclusively discussed in more details. Section 4 to 7 explores state-of-the-art research and limitations in machine-based face recognition, in video base-face recognition and in unsupervised recognition systems. This chapter ends by the summary as well as further thoughts inspired from both cognitive and machine recognition research.

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References

  1. H. Chen, and A. K. Jain: Dental Biometrics: Alignment and Matching of Dental Radiographs. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 8, 2005, pp. 1319–1326

    Article  Google Scholar 

  2. D. Lazer, Ed.: DNA and the Criminal Justice System: The Technology of Justice, MIT Press, Cambridge, MA, 2004

    Google Scholar 

  3. D. A. Norman: Twelve Issues for Cognitive Science. Cognitive Science, Vol. 4, Issue 1, 1980, pp. 1–32

    Article  MathSciNet  Google Scholar 

  4. Halfon N, Shulman E, and Hochstein M, eds.: Brain Development in Early Childhood. Building Community Systems for Young Children, UCLA Center for Healthier Children, Families and Communities, 2001

    Google Scholar 

  5. J.P. de Magalhaes, and A. Sandberg: Cognitive aging as an extension of brain development: A model linking learning, brain plasticity, and neurodegeneration. Mechanisms of Ageing and Development, Vol. 126, 2005, pp. 1026–1033

    Article  Google Scholar 

  6. G.M. Shepherd: The Synaptic Organization of the Brain 5th Edition, Oxford, Oxford Univ. Press, 2004, p.6

    Google Scholar 

  7. C. Koch: Biophysics of Computation. Information Processing in Single Neurons, New York, Oxford Univ. Press, 1999, p.87

    Google Scholar 

  8. Henry Gray: Anatomy of the Human Body, 1918

    Google Scholar 

  9. R. S. Michalski., G. Carbonell, and T. M. Mitchell: Machine Learning: An Artificial Intelligence Approach, Berlin, Springer-Verlag, 1984

    Google Scholar 

  10. P. Thagard: Mind: Introduction to Cognitive Science, 2nd Edition. Cambridge, The MIT Press, 2005

    Google Scholar 

  11. B.A. Wandell: What’s in your mind? Nature Neuroscience, Vol. 11, No. 4, 2008

    Google Scholar 

  12. B.A. Wandell, S.O. Dumoulin, and A. A. Brewer: Visual Field Maps in Human Cortex. Neuron, Vol. 56, No. 2, 2007

    Google Scholar 

  13. T. Serre, L. Wolf, et al.: Robust Object Recognition with Cortex-Like Mechanisms. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 3, 2007, pp. 411–426

    Article  Google Scholar 

  14. L. Wiskott: How does our visual system achieve shift and size invariance? In: J.L. van Hemmen and T.J. Sejnowski, 23 Problems in Systems Neuroscience. Oxford, Oxford University Press, 2006

    Google Scholar 

  15. J. Mutch and D. Lowe: Multiclass object recognition using sparse, localized features. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2006

    Google Scholar 

  16. M. Ranzato, F. Huang, et al: Unsupervised learning of invariant feature hierarchies, with application to object recognition. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2007

    Google Scholar 

  17. D. Mou: Autonomous Face Recognition. Ph.D Dissertation, http://vts.uni-ulm.de/query/longview.meta.asp?document_id=5370, accessed 28 October 2005

  18. M. Johnson, S. Dziurawiec, et al.: Newborns preferential tracking of face-like stimuli and its subsequent decline. Cognition, Vol. 40, 1991, pp. 1–19

    Article  Google Scholar 

  19. J. Sergent, S. Ohta, and B. MacDonald: Functional neuroanatomy of face and object processing: a positron emission tomography study. Brain, Vol. 15, No. 1, 1992, pp. 15–36

    Article  Google Scholar 

  20. N. Kanwisher, J. McDermott, and M. M. Chun: The Fusiform Face Area: A Module in Human Extrastriate Cortex Specialized for Face Perception. The Journal of Neuroscience, Vol. 17, No. 11, 1997, pp. 4302–4311

    Google Scholar 

  21. J. V. Haxby, E. A. Hoffman, and M. I. Gobbini: The distributed human neural system for face perception. Trends in Cognitive Sciences, Vol. 4, Issue 6, 2000, pp. 223–231

    Article  Google Scholar 

  22. E. H. Aylward, J. E. Park, et al.: Brain Activation during Face Perception: Evidence of a Developmental Change. Journal of Cognitive Neuroscience, Vol. 17, Issue 2, 2005

    Google Scholar 

  23. N. Kanwisher, and G. Yovel: The Fusiform Face Area: A Cortical Region Specialized for the Perception of Faces. Philosophical Transactions of the Royal Society of London B: Biological Sciences, Vol. 361, 2006, pp. 2109–2128

    Article  Google Scholar 

  24. M. J. Tarr, and I. Gauthier: FFA: a flexible fusiform area for subordinate-level visual processing automatized by expertise. Nature Neuroscience, Vol. 3, No. 8, 2000

    Google Scholar 

  25. I. Gauthier, and N. K. Logothetis: Is face recognition not so unique, after all? Cognitive Neuropsychology, Vol. 17, 2000, pp. 125–142

    Article  Google Scholar 

  26. M. Riesenhuber and T. Poggio: Neural mechanisms of object recognition. Current Opinion in Neurobiology, Vol. 12, 2002, pp. 162–168

    Article  Google Scholar 

  27. T. J. Andrews and D. Schluppeck: Neural responses to Mooney images reveal a modular representation of faces in human visual cortex. Neuroimage, Vol. 21, Issue 1, 2004

    Google Scholar 

  28. P. Rotshtein, R. N. Henson, et al.: Morphing Marilyn into Maggie dissociates physical and identity face representations in the brain. Nature Neuroscience, Vol. 8, No. 1, 2005

    Google Scholar 

  29. C. G. Gross: Representation of visual stimuli in inferior temporal cortex. Philosophical Transactions of the Royal Society of London B., Vol. 335, 1992, pp. 3–10

    Article  Google Scholar 

  30. A. Mechelli, C.J. Price, et al.: Where bottom-up meets top-down: neuronal interactions during perception and imagery. Cerebral Cortex, Vol. 14, No. 11, 2004

    Google Scholar 

  31. M.R. Johnson, K.J. Mitchell, et al.: A brief thought can modulate activity in extrastriate visual areas: Top-down effects of refreshing just-seen visual stimuli. Neuroimage, Vol. 37, Issue 1, 2007

    Google Scholar 

  32. P. Sinha, B. Balas, et al.: Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About. Proceedings of The IEEE, Vol. 94, No. 11, 2006

    Google Scholar 

  33. M. Dawson: Understanding Cognitive Science. Malden, Blackwell, 1998

    Google Scholar 

  34. A A. Ross, K. Nandakumar and A.K. Jain: Handbook of Multibiometrics. Boston, Springer, 2006

    Google Scholar 

  35. H. Chen, and B. Bhanu: Human Ear Recognition in 3D. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 4, 2007, pp. 718–737

    Article  Google Scholar 

  36. Z. Korotkaya: Biometrics Person Authentication: Odor. <http://www.it.lut.fi/kurssit/>03-04/010970000/ seminars/Korotkaya.pdf, accessed 08 December 2005

  37. R. Palaniappan, and D. P. Mandic: Biometrics from Brain Electrical Activity: A Machine Learning Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 4, 2007, pp. 738–742

    Article  Google Scholar 

  38. <http://www.biometricgroup.com/reports/public/market_report.html>, accessed 15 December 2007

  39. Information Technology. Biometric Data Interchange Formats. Iris Image Data. ISO/IEC 19794-6:2005

    Google Scholar 

  40. Faulds, Henry: On the Skin-furrows of the Hand. Nature, Macmillan and Co., London, October 28, 1880, p. 605

    Google Scholar 

  41. Herschel, W. J.: Skin Furrows of the Hand. Nature, Macmillan and Co., London, Nov. 25, 1880, p. 76

    Google Scholar 

  42. Galton, Sir Francis: Finger Prints. Macmillan and Co., London, 1892.

    Google Scholar 

  43. <http://www.fbi.gov/hq/cjisd/iafis.htm>, accessed 15 December 2007

  44. D Maltoni, D Maio, et al.: Handbook of Fingerprint Recognition, New York, Springer, 2003

    MATH  Google Scholar 

  45. E. Hjelmas and B.K. Low: Face Detection: A Survey. Computer Vision and Image Understanding, 2001, Vol. 83, No. 3, 2001, pp. 236–274

    Article  MATH  Google Scholar 

  46. M. Yang, D.J. Kriegman, and N. Ahuja: Detecting faces in images: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 1, 2002, pp. 34–58

    Article  Google Scholar 

  47. M. Yang: Recent Advances in Face Detection. IEEE ICIP 2004 Tutorial, Cambridge, UK, <http://vision.ai.uiuc.edu/mhyang/face-detection-survey.html>, accessed 13 October 2005

  48. S. Gong, S. McKenna, and A. Psarrou: Dynamic Vision: From Images to Face Recognition, London, Imperial College Press, 2000

    Google Scholar 

  49. K. C. Yow and R. Cipolla: Feature-Based Human Face Detection. Image and Vision Computing, Vol. 15, No. 9, 1997, pp. 713–735

    Article  Google Scholar 

  50. J. Yang and A. Waibel, “A Real-Time Face Tracker”, Proceedings of the 3rd Workshop on Applications of Computer Vision (WACV’96), 1996, pp. 142–147

    Google Scholar 

  51. G. Yang and T. Huang: Human Face Dtection in Complex Background. Pattern Recognition, Vol. 27, No. 1, 1994, pp. 53–63

    Article  Google Scholar 

  52. C. Kotropoulos and I. Pitas: Rule-Based Face Detection in Frontal Views. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’97), Vol. 4, 1997, pp. 2537–2540

    Google Scholar 

  53. B. Cumbers (2003): Passive Biometric Customer Identification and Tracking System. U.S. Patent, 6554705, April 2003

    Google Scholar 

  54. K. Sung and T. Poggio: Example-Based learning for view-Based Human Face Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 1, 1998, pp. 39–51

    Article  Google Scholar 

  55. H. Rowley, S. Baluja, and T. Kanade: Neural Network-Based Face Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 1, 1998, pp. 23–38

    Article  Google Scholar 

  56. R, Fér aud, O. Bernier, et al.: A Fast and Accurate Face Detector Based on Neural Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, 2001, pp. 42–53

    Article  Google Scholar 

  57. E. Osuna, R. Freund, and F. Girosi: Training Support Vector Machines: An Application to Face Detection. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1997, pp. 130–136

    Google Scholar 

  58. H. Schneiderman and T. Kanade: A Statistical Method for 3D Object Detection Applied to Faces and Cars. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Vol. 1, 2000, pp. 746–751

    Google Scholar 

  59. M. Yang, D. Roth, and N. Ahuja: A SnoW-Based Linear Subspaces for Face Detection. In: S. Solla, T. Leen, and K. Müller, eds. Advances in Neural Information Processing System 12, MIT Press, 2000, pp. 855–861

    Google Scholar 

  60. P. Viola and M. Jones: Robust Real-time Object Detection. IEEE ICCV Workshop on Statistical and Computational Theories of Vision, July 13, 2001

    Google Scholar 

  61. M. Jones, P. Viola: Fast Multi-view Face Detection. Mitsubishi Electric Research Laboratories Technical Reports, TR2003-96, 2003, <http://www>. merl.com/ reports/ docs/ TR2003-96.pdf, accessed 12 October 2005

  62. Z. Zhang, L. Zhu, et al.: Real-Time Multi-View Face Detection. Proceedings of Fifth IEEE International Conference on Automatic Face and Gesture Recognition, May 2002, pp. 149–154

    Google Scholar 

  63. M. Turk and A. Pentland: Eigenfaces for Recognition. Journal of Cognitive Neuroscience, Vol. 3, No. 1, 1991, pp. 72–86

    Article  Google Scholar 

  64. S. Palanive, B.S. Venkatesh, and B. Yegnanarayana: Real time face recognition system using autoassociative neural network models. IEEE Conference Proceedings on Acoustics, Speech, and Signal Processing (ICASSP′03), Vol. 2, 2003, pp. 833–836

    Google Scholar 

  65. T. kim, S. Lee, et al.: Integrated approach of multiple face detection for video surveillance. Proceedings of IEEE 16th Conference on Pattern Recognition, Vol. 2, 2002, pp. 394–397

    Google Scholar 

  66. D. Butler, C. McCool, et al.: Robust Face Localisation Using Motion. Colour & Fusion Proceedings of Digital Image Computing: Techniques and Applications (DICTA 2003), 2003, pp. 899–908

    Google Scholar 

  67. C. Wren, A. Azerbayejani, et al.: Pfinder: A Real-Time Tracking of Human Body. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, 1997, pp. 780–785

    Article  Google Scholar 

  68. Y. Raja, S.J. McKenna, and S. Gong: Tracking and Segmenting People in Varying Lighting Conditions Using Color. Proceedings of IEEE Conference on Automatic Face and Gesture Recognition, 1998, pp. 228–233

    Google Scholar 

  69. K. Schwerdt and J. Crowley: Robust Face Tracking Using Colour. Proceedings of IEEE Conference on Automatic Face and Gesture Recognition, 2000, pp. 90–95

    Google Scholar 

  70. S. Birchfield: Elliptical Head Tracking Using Intensity Gradients and Color Histograms. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 1998, pp. 232–237

    Google Scholar 

  71. R.C. Verma, C. Schmid, and K. Mikolajczyk: Face detection and tracking in a video by propagating detection probabilities. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, Issue 10, 2003, pp. 1215–1228

    Article  Google Scholar 

  72. R. Chellappa, C.L. Wilson and S. Sirohey: Human and Machine Recognition of Faces: A Survey. Proceedings of IEEE, Vol. 83, No. 5, 1995, pp. 705–740

    Article  Google Scholar 

  73. W. Zhao, R. Chellappa, et al.: Face Recognition: A Literature Survey. Technical Report (CS-TR-4167R), University of Maryland. <ftp://ftp.cfar.umd.edu>, accessed 20 August 2005

  74. K. Bowyer, K. Chang, and P. Flynn: A survey of Approaches and Challenges in 3D and Multi-Modal 3D 2D Face Recognition. IEEE Transactions on Computer Vision and Image Understanding, Vol. 101, No. 1, 2006, pp. 1–15

    Article  Google Scholar 

  75. X. Lu, and A. Jain: Deformation Modeling for Robust 3D Face Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 8, 2008, pp. 1346–1357

    Article  Google Scholar 

  76. A. Franco, D. Maio and D. Maltoni: 2D Face Recognition based on Supervised Subspace Learning from 3D Models. Pattern Recognition, Vol. 41, No. 12, 2008, pp. 3822–3833

    Article  MATH  Google Scholar 

  77. P. J. Phillips, P. Rauss and S. Der: FERET (Face Recognition Technology) Recognition Algorithm Development and Test Report. Technical Report ARL-TR 995, U.S. Army Research Laboratory, 1996

    Google Scholar 

  78. P. J. Phillips, H. Moon, et al.: The FERET Evaluation Method for Face Recognition Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, 2000, pp. 1090–1104

    Article  Google Scholar 

  79. D. M. Blackburn, M. Bone and P.J. Phillips: FRVT 2000 Evaluation Report. Technical Report, Feb. 16th, 2001, <http://www.frvt.org>, accessed 29 March 2007

  80. P. J. Phillips, P. Grother, et al.: FRVT 2002 Evaluation Report, Technical Report, March, 2003, <http://www.frvt.org>, accessed 29 March 2007

  81. P. J. Phillips, W. T. Scruggs, et al.: FRVT 2006 and ICE 2006 Large-Scale Results. Technical Report, March 2007, <http://www.frvt.org>, accessed 29 March 2007

  82. V. Blanz, S. Romdhami, and T. Vetter: Face identification across different poses and illuminations with a 3D morphable model. Proceedings of International Conference on Automatic Face and Gesture Recognition, 2002, pp. 202–207

    Google Scholar 

  83. V. Blanz and T. Vetter: Face recognition based on fitting a 3D morphable model. IEEE Transactions on Pattern Analysis and Machine Intelligence, No. 25, 2003, pp. 106–1074

    Article  Google Scholar 

  84. V. Bruce: Recognizing Faces. London, Lawrence Erlbaum Associates, 1988

    Google Scholar 

  85. V. Bruce, P.J.B. Hancock, and A.M. Burton: Human Face Perception and Identification. In: Face Recognition: From Theory to Applications, Berlin, Springer-Verlag, 1998, pp. 51–72

    Google Scholar 

  86. M.S, Bartlett, J.R. Movellan and T.J. Sejnowski: Face Recognition by Independent Component Analysis. IEEE Transactions on Neural Networks, Vol. 13, No. 6, 2002, pp. 1450–1464

    Article  Google Scholar 

  87. D.L. Swets and J.J. Weng: Using Discriminant Eigenfeatures for Image Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 18, No. 8, 1996, pp. 831–836

    Article  Google Scholar 

  88. P. Belhumeur, J.P. Hespanha, and D.J. Kriegman: Eigenfaces vs. Fisherfaces: recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, 1997, pp. 711–720

    Article  Google Scholar 

  89. A.V. Nefian and H.H. Hayes III: Hidden Markov Models for Face Recognition. IEEE International Conference on Acoustic, Speech and Signal Processing, Vol. 5, 1998, pp. 2721–2724

    Google Scholar 

  90. V. V. Kohir and U. B. Desai: Face recognition using DCT-HMM approach. Workshop on Advances in Facial Image Analysis and Recognition Technology (AFIART), June 1998

    Google Scholar 

  91. R. Tjahyadi, W. Liu, and S. Venkatesh: Application of the DCT Energy Histogram for Face Recognition. Proceedings of the 2nd International Conference on Information Technology for Application (ICITA 2004), 2004, pp. 305–310

    Google Scholar 

  92. H. Kang, T. F. Cootes, and C. J. Taylor: A comparison of face verification algorithms using appearance models. Proceedings of The British Machine Vision Conference, Vol. 2, 2002, pp. 477–486

    Google Scholar 

  93. X. Lu, Y. Wang, and A. K. Jain: Combining classifiers for face recognition. Proceedings of the IEEE International Conference on Multimedia & Expo, Vol. 3, July 2003, pp. 13–16

    Google Scholar 

  94. R. Singh, M. Vatsa, et al.: A Mosaicing Scheme for Pose Invariant Face Recognition. IEEE Transactions on Systems, Mans and Cybernetics-B, Special Issue on Biometrics, Vol. 37, Issue 5, 2007, pp. 1212–1225

    Article  Google Scholar 

  95. M. Bicego, U. Castellani, V. Murino: Using Hidden Markov Models and Wavelets for face recognition. Proceedings of IEEE International Conference on Image Analysis and Processing (ICIAP03), 2003, pp. 52–56

    Google Scholar 

  96. L. Wiskott, J. Fellous, et al.: Face Recognition by Elastic Bunch Graph Matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, 1997, pp. 775–779

    Article  Google Scholar 

  97. C. Liu and H. Wechsler: A Gabor Feature Classifier for Face Recognition. Proceedings of Eighth IEEE International Conference on Computer Vision, Vol. 2, 2001, pp. 270–275

    Article  Google Scholar 

  98. B.A. Draper, K. Baek, et al.: Recognizing faces with PCA and ICA. Computer Vision and Image Understanding, Vol. 91, No. 1, 2003, pp. 115–137

    Article  Google Scholar 

  99. A.M. Martinez and A.C. Kak: PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 2, 2001, pp. 228–233

    Article  Google Scholar 

  100. M.H. Yang: Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods. Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition (FG’02), May 2002, pp. 215–220

    Google Scholar 

  101. J. Lu, K.N.Plataniotis, and A.N. Venetsanopoulos: Face Recognition Using Kernel Direct Discriminant Analysis Algorithms. IEEE Transactions on Neural Networks, Vol. 14, No. 1, 2003, pp. 117–126

    Article  Google Scholar 

  102. Y. Zhang, L. Lang and O. Hamsici: Subspace Analysis for Facial Image Recognition: A Comparative Study. <http://www.stat.ohio-state.edu/~goel/> STATLEARN/, accessed 12 October 2006.

  103. G. Guo, S. Z. Li, and C. Kapluk: Face recognition by support vector machines. Image and Vision Computing, Special Issue on Artificial Neural Networks for Image Analysis and Computer Vision, Vol. 19, No. 9-10, 2001, pp. 631–638

    Google Scholar 

  104. B. Heisele, P. Ho and T. Poggio: Face Recognition with Support Vector Machines: Global versus Component-based Approach. Proceedings of IEEE International Conference on Computer Vision, 2001, pp. 688–694

    Google Scholar 

  105. J. Huang, V. Blanz, and B. Heisele: Face Recognition Using Component-Based SVM Classification and Morphable Models. SVM 2002, 2002, pp. 334–341

    Google Scholar 

  106. S. Lawrence, C.L. Giles, et al.: Face Recognition: A Convolutional Neural Network Approach. IEEE Transactions on Neural Networks, Vol. 8, No. 1, 1997, pp. 98–113

    Article  Google Scholar 

  107. T. Kurita, M. Pic, and T. Takahashi: Recognition and Detection of Occluded Faces by A Neural Network Classifier with Recursive Data Reconstruction. Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS’03), 2003, pp. 53–58

    Google Scholar 

  108. Xiaoming Liu, and Tsuhan Chen: Video-Based Face Recognition Using Adaptive Hidden Markov Models. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR’03), 2003, pp. 340–345

    Google Scholar 

  109. V. Krueger and S. Zhou: Exemplar-based Face Recognition from Video. Fifth IEEE International Conference on Automatic Face and Gesture Recognition, May 21–22, 2002, pp. 175–180

    Google Scholar 

  110. A: Hadid and M. Pietikäinen: From Still Image to Video-Based Face Recognition: An Experimental Analysis. Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition (FGR’04), 2004, pp. 813–818

    Google Scholar 

  111. X. Tang and Z. Li: Video Based Face Recognition Using Multiple Classifiers. Proceedings of IEEE International Conference on Automatic Face and Gesture Recognition (FGR’04), 2004, pp. 345–349

    Google Scholar 

  112. O. Arandjelovic and R. Cipolla: Face Recognition from Face Motion Manifolds using Robust Kernel Register-Average Distance. IEEE International Conference on Computer Vision and Pattern Recognition Workshop (CVPRW’04), Vol. 5, 2004, p.70

    Google Scholar 

  113. J. Weng and W. Hwang: Toward Automation of Learning: The State Self-Organization Problem for a Face Recognizer. Proceedings of the IEEE Conference on Automatic Face and Gesture Recognition, 1998, pp. 384–389

    Google Scholar 

  114. J. Weng, C. Evans, and W. Hwang: An Incremental Learning Method for Face Recognition under Continuous Video Stream. Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, 2000, pp. 251–256

    Google Scholar 

  115. K. Okada, L. Kite, and C. von der Malsburg: An Adaptive Person Recognition System. Proceedings of the IEEE International Workshop on Robot-Human Interactive Communication, 2001, pp. 436–441

    Google Scholar 

  116. Lijin Aryananda: Recognizing and Remembering Individuals: Online and Unsupervised Face Recognition for Humanoid Robot. Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2002), Vol. 2, 2002, pp. 1202–1207

    Article  Google Scholar 

  117. B. Raytchev, H. Murase: Unsupervised Face Recognition by Associative Chaining. Pattern Recognition, Vol. 36, No. 1, 2003, pp. 245–257

    Article  MATH  Google Scholar 

  118. Q. Xiong and C. Jaynes: Mugshot Database Acquisition in Video Surveillance Networks Using Incremental Auto-Clustering Quality Measures. Proceedings of the 2003 IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS’03), 2003, pp. 191–198

    Google Scholar 

  119. H. Wechsel, V. Kakkad, et al.: Automatic Video-Based Person Authentication Using the RBF Network. Proceedings of 1st International Conference on Audio And Videobased Biometric Person Authentication, 1997, pp. 85–92

    Google Scholar 

  120. C. Lambert (1991): Autonomous Face Recognition Machine. U.S. Patent, 5012522, April, 1991

    Google Scholar 

  121. Y.T. Lin (2002): Adaptive Facial Recognition System and Method. U.S. Patent application publication, US2002/0136433, Sep. 26, 2002

    Google Scholar 

  122. J.L. Center JR (2003).: Real-time Facial Recognition and Verification System. U.S. Patent application publication, US2003/0059124, Mar. 27, 2003

    Google Scholar 

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    Dengpan Mou

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Mou, D. (2010). Fundamentals and Advances in Biometrics and Face Recognition. In: Machine-based Intelligent Face Recognition. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00751-4_2

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